Efficient incorporation of fatigue damage constraints in wind turbine blade optimization

Ingersoll, Bryce; Ning, Andrew

doi:10.1002/we.2473

Cited by 15 publications

(12 citation statements)

References 34 publications

(55 reference statements)

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“…where N fail is the number of cycles to failure, SF is a safety factor, and m is the material-dependent Wöhler exponent. For composite turbine blades, it is typically assumed that m = 10, which is the value used in this study (Ingersoll and Ning, 2020). Miner's rule was then used to calculate the damage accumulated by a turbine over a 25-year lifespan, shown in Eq.…”

Section: Calculate Damagementioning

confidence: 99%

A model to calculate fatigue damage caused by partial waking during wind farm optimization

Stanley¹,

King

Bay

et al. 2022

Wind Energ. Sci.

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Abstract. Wind turbines in wind farms often operate in waked or partially waked conditions, which can greatly increase the fatigue damage. Some fatigue considerations may be included, but currently a full fidelity analysis of the increased damage a turbine experiences in a wind farm is not considered in wind farm layout optimization because existing models are too computationally expensive. In this paper, we present a model to calculate fatigue damage caused by partial waking on a wind turbine that is computationally efficient and can be included in wind farm layout optimization. The model relies on analytic velocity, turbulence, and load models commonly used in farm research and design, and it captures some of the effects of turbulence on the fatigue loading. Compared to high-fidelity simulation data, our model accurately predicts the damage trends of various waking conditions. We also perform example wind farm layout optimizations with our presented model in which we maximize the annual energy production (AEP) of a wind farm while constraining the damage of the turbines in the farm. The results of our optimization show that the turbine damage can be significantly reduced, more than 10 %, with only a small sacrifice of around 0.07 % to the AEP, or the damage can be reduced by 20 % with an AEP sacrifice of 0.6 %.

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Section: Calculate Damagementioning

confidence: 99%

A model to calculate fatigue damage caused by partial waking during wind farm optimization

Stanley¹,

King

Bay

et al. 2022

Wind Energ. Sci.

Self Cite

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show abstract

“…Surrogate modeling to optimize component design for reliability in wind turbine structural components has been widely used as a way to meet design standards and to ensure turbine reliability over a complete set of design load constraints while decreasing the computational expense for designers. Recent studies developed surrogates for blade loads, [33][34][35][36][37] support structure loads, 35,[38][39][40][41][42] and main shaft torsion. 43 Quantifying the uncertainty of those surrogates is also presented.…”

Section: Wind Plant Layout Optimization Load Simulation and Surrogate Modelingmentioning

confidence: 99%

Reliability‐based layout optimization in offshore wind energy systems

et al. 2021

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Existing methods for optimizing wind array layouts typically use power or cost objectives and rarely consider reliability-based objectives. Component and system failure rates, however, are dependent on location-specific wind conditions, are influenced by array layout and wake interactions, and have a direct and significant impact on capital costs, operational costs, and power production. Although wind power plant models exist that calculate wind loads with sufficient resolution to capture component loading dynamics from wind conditions, they are computationally expensive and thus not suitable for research applications requiring many evaluations, particularly optimization. This study describes the development of computationally efficient, reliability-based layout optimization methods, enabling us to explore the relationship between component reliability and layout optimization. These methods include the surrogate modeling of the planet bearing life based on varying wind conditions simulated in FAST.Farm and the formulation of reliability-based objectives based on failure cost and power production models. Through demonstration of this method, we explore how wind conditions, objective functions, and capacity density influence reliability-based layout optimization. Results indicate that considering reliability alongside power production can reduce failure costs associated with replacement costs and downtime whilemaintaining or improving power production. Our conclusions highlight the opportunity for wind power plant developers to integrate reliability and operational expenditures alongside performance and capital expenditure objectives in plant design and development to improve plant performance and costs.

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Section: Calculate Damagementioning

confidence: 99%

A Model to Calculate Fatigue Damage Caused by Partial Waking during Wind Farm Optimization

Stanley¹,

King

Bay

et al. 2020

Preprint

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Abstract. Wind turbines in wind farms often operate in waked or partially waked conditions, which can greatly increase the fatigue damage. Some fatigue considerations may be included, but currently a full fidelity analysis of the increased damage a turbine experiences in a wind farm is not considered in wind farm layout optimization because existing models are too computationally expensive. In this paper, we present a model to calculate fatigue damage caused by partial waking on a wind turbine that is computationally efficient and can be included in wind farm layout optimization. The model relies on analytic velocity, turbulence, and loads models commonly used in farm research and design, and captures some of the effects of turbulence on the fatigue loading. Compared to high-fidelity simulation data, our model accurately predicts the damage trends of various waking conditions. We also perform example wind farm layout optimizations with our presented model in which we maximize the annual energy production (AEP) of a wind farm while constraining the damage of the turbines in the farm. The results of our optimization show that the turbine damage can be significantly reduced, more than 10 %, with only a small sacrifice of around 0.07 % to the AEP, or the damage can be reduced by 20 % with an AEP sacrifice of 0.6 %.

show abstract

Efficient incorporation of fatigue damage constraints in wind turbine blade optimization

Cited by 15 publications

References 34 publications

A model to calculate fatigue damage caused by partial waking during wind farm optimization

A model to calculate fatigue damage caused by partial waking during wind farm optimization

Reliability‐based layout optimization in offshore wind energy systems

A Model to Calculate Fatigue Damage Caused by Partial Waking during Wind Farm Optimization

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